code: def test_score_ndarrays():
      X = np.ones((10, 5))
      y = np.ones(10)
  
      sgd = SGDClassifier(tol=1e-3)
      inc = Incremental(sgd, scoring="accuracy")
  
      inc.partial_fit(X, y, classes=[0, 1])
      inc.fit(X, y, classes=[0, 1])
  
      assert inc.score(X, y) == 1
  
      dX = da.from_array(X, chunks=(2, 5))
      dy = da.from_array(y, chunks=2)
      assert inc.score(dX, dy) == 1
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from dask.array.utils import assert_eq
  from scipy.sparse import csr_matrix
  from sklearn.base import clone
  from sklearn.linear_model import SGDClassifier, SGDRegressor
  from sklearn.pipeline import make_pipeline
  import dask_ml.feature_extraction.text
  import dask_ml.metrics
  from dask_ml.metrics.scorer import check_scoring
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 8 for calling a global function with example usage: test_score_ndarrays() without return types